import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import BaggingRegressor

from sklearn.metrics import root_mean_squared_error as rmse

from tqdm import tqdm

import dill
import random

import salishsea_tools.viz_tools as sa_vi

def datasets_preparation(dataset, dataset2):
    
    drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
        np.ravel(dataset['Temperature_(15m-100m)']), 
        np.ravel(dataset['Salinity_(0m-15m)']),
        np.ravel(dataset['Salinity_(15m-100m)']),
        np.ravel(dataset2['Summation_of_solar_radiation']),
        np.ravel(dataset2['Mean_wind_speed']),
        np.ravel(dataset2['Mean_air_temperature'])
        ])
    indx = np.where(~np.isnan(drivers).any(axis=0))
    drivers = drivers[:,indx[0]]

    diat = np.ravel(dataset['Diatom'])
    diat = diat[indx[0]]

    return(drivers, diat, indx)

def regressor (inputs, targets):
    
    inputs = inputs.transpose()
    
    # Regressor
    X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)

    model = MLPRegressor(hidden_layer_sizes=300)
    model = make_pipeline(StandardScaler(), model)
    regr = BaggingRegressor(model, n_estimators=24, n_jobs=-1).fit(X_train, y_train)

    return (regr)

def regressor2 (inputs, targets, variable_name):

    inputs2 = inputs.transpose()

    outputs_test = regr.predict(inputs2)
   
    m = scatter_plot(targets, outputs_test, variable_name) 
    r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
    rms = rmse(targets, outputs_test)

    return (r, rms, m)

def regressor3 (inputs, targets):
  
    inputs2 = inputs.transpose()

    outputs_test = regr.predict(inputs2)
   
    # compute slope m and intercept b
    m, b = np.polyfit(targets, outputs_test, deg=1)
    
    r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
    rms = rmse(targets, outputs_test)

    return (r, rms, m)

def regressor4 (inputs, targets, variable_name):

    inputs2 = inputs.transpose()
    
    outputs = regr.predict(inputs2)

    # Post processing
    indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
    indx2[indx[0]] = outputs
    model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))

    m = scatter_plot(targets, outputs, variable_name + str(dates[i].date())) 

    # Preparation of the dataarray 
    model = xr.DataArray(model,
        coords = {'y': diat_i.y, 'x': diat_i.x},
        dims = ['y','x'],
        attrs=dict( long_name = variable_name + "Concentration",
        units="mmol m-2"),)
                        
    plotting3(targets, model, diat_i, variable_name)

def printing (targets, outputs, m):

    print ('The amount of data points is', outputs.size)
    print ('The slope of the best fitting line is ', np.round(m,3))
    print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
    print (' The mean square error is:', rmse(targets,outputs))

def scatter_plot(targets, outputs, variable_name):

    # compute slope m and intercept b
    m, b = np.polyfit(targets, outputs, deg=1)

    printing(targets, outputs, m)

    fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')

    ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)

    lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
        np.max([ax[0].get_xlim(), ax[0].get_ylim()])]

    # plot fitted y = m*x + b
    ax[0].axline(xy1=(0, b), slope=m, color='r')

    ax[0].set_xlabel('targets')
    ax[0].set_ylabel('outputs')
    ax[0].set_xlim(lims)
    ax[0].set_ylim(lims)
    ax[0].set_aspect('equal')

    ax[0].plot(lims, lims,linestyle = '--',color = 'k')

    h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet', 
        range=[lims,lims], cmin=0.1, norm='log')
    
    ax[1].plot(lims, lims,linestyle = '--',color = 'k')

    # plot fitted y = m*x + b
    ax[1].axline(xy1=(0, b), slope=m, color='r')

    ax[1].set_xlabel('targets')
    ax[1].set_ylabel('outputs')
    ax[1].set_aspect('equal')

    fig.colorbar(h[3],ax=ax[1], location='bottom')

    fig.suptitle(variable_name)

    plt.show()

    return (m)

def plotting(variable, name):

    plt.plot(years,variable, marker = '.', linestyle = '')
    plt.xlabel('Years')
    plt.ylabel(name)
    plt.show()

def plotting2(variable,title):
    
    fig, ax = plt.subplots()

    scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)

    ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
    fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
    
    fig.show()

def plotting3(targets, model, variable, variable_name):

    fig, ax = plt.subplots(2,2, figsize = (10,15))

    cmap = plt.get_cmap('cubehelix')
    cmap.set_bad('gray')

    variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration  [mmol m-2]'})
    model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration  [mmol m-2]'})
    ((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration  [percentage]'})

    plt.subplots_adjust(left=0.1,
        bottom=0.1, 
        right=0.95, 
        top=0.95, 
        wspace=0.35, 
        hspace=0.35)

    sa_vi.set_aspect(ax[0,0])
    sa_vi.set_aspect(ax[0,1])
    sa_vi.set_aspect(ax[1,0])


    ax[0,0].title.set_text(variable_name + ' (targets)')
    ax[0,1].title.set_text(variable_name + ' (outputs)')
    ax[1,0].title.set_text('targets - outputs')
    ax[1,1].axis('off')

    fig.suptitle(str(dates[i].date()))

    plt.show()

ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds2 = xr.open_dataset('/data/ibougoudis/MOAD/files/external_inputs.nc')

ds = ds.isel(time_counter = (np.arange(0, len(ds.time_counter),2)), 
    y=(np.arange(ds.y[0], ds.y[-1], 5)), 
    x=(np.arange(ds.x[0], ds.x[-1], 5)))

ds2 = ds2.isel(time_counter = (np.arange(0, len(ds2.time_counter),2)), 
    y=(np.arange(ds2.y[0], ds2.y[-1], 5)), 
    x=(np.arange(ds2.x[0], ds2.x[-1], 5)))

dates = pd.DatetimeIndex(ds['time_counter'].values)

drivers, diat, _ = datasets_preparation(ds, ds2)

regr = regressor(drivers, diat)

years = range (2007,2024)

r_all = []
rms_all = []
slope_all = []

for year in tqdm(range (2007,2024)):
    
    dataset = ds.sel(time_counter=str(year))
    dataset2 = ds2.sel(time_counter=str(year))
    
    drivers, diat, _ = datasets_preparation(dataset, dataset2)

    r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
    
    r_all.append(r)
    rms_all.append(rms)
    slope_all.append(m)
    
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')

  0%|          | 0/17 [00:00<?, ?it/s]

The amount of data points is 70794
The slope of the best fitting line is  0.503
The correlation coefficient is: 0.747
 The mean square error is: 0.10649864509588611

  6%|▌         | 1/17 [00:02<00:44,  2.81s/it]

The amount of data points is 70794
The slope of the best fitting line is  0.532
The correlation coefficient is: 0.724
 The mean square error is: 0.10102927170359083

 12%|█▏        | 2/17 [00:05<00:41,  2.75s/it]

The amount of data points is 68931
The slope of the best fitting line is  0.532
The correlation coefficient is: 0.781
 The mean square error is: 0.12813719987335395

 18%|█▊        | 3/17 [00:08<00:38,  2.72s/it]

The amount of data points is 70794
The slope of the best fitting line is  0.448
The correlation coefficient is: 0.691
 The mean square error is: 0.10684976373474256

 24%|██▎       | 4/17 [00:10<00:35,  2.73s/it]

The amount of data points is 68931
The slope of the best fitting line is  0.628
The correlation coefficient is: 0.818
 The mean square error is: 0.0906291753594586

r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])

for i in tqdm(range (0, len(ds.time_counter))):
    
    dataset = ds.isel(time_counter=i)
    dataset2 = ds2.isel(time_counter=i)

    drivers, diat, _ = datasets_preparation(dataset, dataset2)

    r, rms, m = regressor3(drivers, diat)

    r_all2 = np.append(r_all2,r)
    rms_all2 = np.append(rms_all2,rms)
    slope_all2 = np.append(slope_all2,m)

plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')

100%|██████████| 640/640 [00:39<00:00, 16.10it/s]

maps = random.sample(range(0,len(ds.time_counter)),10)

for i in tqdm(maps):

    dataset = ds.isel(time_counter=i)
    dataset2 = ds2.isel(time_counter=i)
    drivers, diat, indx = datasets_preparation(dataset, dataset2)

    diat_i = dataset['Diatom']

    regressor4(drivers, diat, 'Diatom ')

  0%|          | 0/10 [00:00<?, ?it/s]

The amount of data points is 1863
The slope of the best fitting line is  0.86
The correlation coefficient is: 0.827
 The mean square error is: 0.03942991549087954

 10%|█         | 1/10 [00:00<00:08,  1.09it/s]

The amount of data points is 1863
The slope of the best fitting line is  0.253
The correlation coefficient is: 0.661
 The mean square error is: 0.17289962397879405

 20%|██        | 2/10 [00:02<00:08,  1.04s/it]

The amount of data points is 1863
The slope of the best fitting line is  0.491
The correlation coefficient is: 0.654
 The mean square error is: 0.10885201560907953

 30%|███       | 3/10 [00:02<00:06,  1.04it/s]

The amount of data points is 1863
The slope of the best fitting line is  0.382
The correlation coefficient is: 0.535
 The mean square error is: 0.13195135003817646

 40%|████      | 4/10 [00:03<00:05,  1.07it/s]

The amount of data points is 1863
The slope of the best fitting line is  0.287
The correlation coefficient is: 0.722
 The mean square error is: 0.24203411696630844

Script to reproduce years based on a model trained with random points¶

Importing¶

Datasets Preparation¶

Regressor¶

Regressor 2¶

Regressor 3¶

Regressor 4¶

Printing¶

Scatter Plot¶

Plotting¶

Plotting 2¶

Plotting 3¶

Training (Random Points)¶

Other Years (Anually)¶

Other Years (Daily)¶

Daily Maps¶